In [1]:
import sys
import os
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../../'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/'))
sys.path.insert(0, os.path.abspath('/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/ibm/'))
In [2]:
import numpy as np

import qiskit
provider = qiskit.IBMQ.load_account()
from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.algorithms import QAOA
from shared.QiskitMaxcut import *
from ibm.ibm_parameters import *

from matplotlib import pyplot as plt
%matplotlib inline

from ibm_landscape_processes import *

ibmqfactory.load_account:WARNING:2021-08-21 11:14:39,639: Credentials are already in use. The existing account in the session will be replaced.
In [3]:
%load_ext autoreload
%autoreload 2
In [4]:
# ---- Define graph and MaxCut ----
graph = load_nx_graph_from("/home/hm-tlacherm/qlm_notebooks/notebooks_1.2.1/notebooks/master_thesis_qaoa/data/graphs/16_nodes/graph_16_33_01_w.txt")
max_cut = Maxcut(graph)
max_cut_qubo = max_cut.to_qubo()
max_cut.draw()
In [5]:
# ---- Define step size and gamma, beta values ----
step_size = 0.1
a_gamma = np.arange(0, np.pi, step_size)
b_beta = np.arange(0, np.pi, step_size)
In [6]:
gammas, betas = np.meshgrid(a_gamma, b_beta)
In [7]:
# ---- execute for all values QAOA and get result matrix  ----
landscape = run_all(gammas, betas, max_cut)

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In [8]:
print(landscape)
plt.matshow(landscape)
plt.show()

[[ -88.68000031  -88.41312408  -88.44975281 ...  -88.63487244
   -88.69625092  -88.27649689]
 [ -88.89787292  -77.05825043  -67.4103775  ... -112.84649658
  -104.28937531  -93.19237518]
 [ -88.53012848  -86.06424713  -83.65562439 ...  -93.71299744
   -91.95024872  -89.18399811]
 ...
 [ -88.67462158  -89.36199951  -90.9123764  ...  -85.93887329
   -86.56300354  -88.26850128]
 [ -88.38899994  -88.44400024  -87.91812134 ...  -89.91925049
   -88.72899628  -88.71437836]
 [ -88.90325165  -88.15087128  -88.30112457 ...  -89.00187683
   -88.93262482  -88.26287842]]
In [9]:
# Mean of landscape
np.mean(landscape)
Out[9]:
-88.49827872216702
In [10]:
# Minimium 
np.min(landscape)
Out[10]:
-118.09700012207031
In [11]:
# Display Coordinates of Minimum 
np.unravel_index(np.argmin(landscape), landscape.shape)
Out[11]:
(1, 12)
In [12]:
# Gamma and beta value of Minimium
gamma, beta = np.unravel_index(np.argmin(landscape), landscape.shape)
opt_gamma = gamma * step_size
opt_beta = beta * step_size
print(f"Opt.Gamma: {opt_gamma}, Opt.Beta: {opt_beta}")

Opt.Gamma: 0.1, Opt.Beta: 1.2000000000000002
In [13]:
# Save result matrix 
with open('landscape_simulator_paper_weights_results.npy', 'wb') as f:
    np.save(f, landscape)
In [14]:
import plotly.graph_objects as go
In [15]:
# Plot landscape in 3D 
a_gamma = np.arange(0, np.pi, step_size)
b_beta = np.arange(0, np.pi, step_size)
fig = go.Figure(data=go.Surface(z=landscape, x=a_gamma, y=b_beta))

fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor='limegreen', project_z=True))


fig.update_layout(title="QAOA MaxCut", scene=dict(
    xaxis_title="gamma",
    yaxis_title="beta",
    zaxis_title="mean"
))
In [16]:
# Plot Heatmap 
fig = go.Figure(data=go.Heatmap(z=landscape, x=b_beta, y=a_gamma, type = 'heatmap', colorscale = 'viridis'))

# Update Layout
fig.update_layout(title="QAOA MaxCut", width=700, height=700, xaxis_title="beta", yaxis_title="gamma")

# Display Global Minimium 
fig.add_trace(
    go.Scatter(mode="markers", x=[opt_beta], y=[opt_gamma], marker_symbol=[204], text = [landscape[gamma,beta]],
                   marker_color="red",  hovertemplate="x: %{x}<br>y: %{y}<br> z: %{text:.2f}<extra></extra>", 
                   marker_line_width=1, marker_size=16))
In [17]:
# Display Optimizer Results

# Display path 
#fig.add_trace(
#    go.Scatter(mode="lines", x=gammas, y=betas, marker_symbol=[200],
#                   marker_color="white", marker_line_width=1, marker_size=8)
#)

# Display start point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[0]], y=[betas[0]], marker_symbol=[204],
#                   marker_color="gray", 
#                   marker_line_width=1, marker_size=16))

# Display end point
#fig.add_trace(
#    go.Scatter(mode="markers", x=[gammas[-1]], y=[betas[-1]], marker_symbol=[204],
#                   marker_color="green", 
#                   marker_line_width=1, marker_size=16))
In [18]:
# Plot Optimizer History
#fig = go.Figure(data=go.Scatter(x=counts, y=values))
#fig.update_layout(xaxis_title="Evaluation Counts", yaxis_title="Evaluated Mean", title="Optimizer")
#fig.show()